code: def __sample_speaker(self):
      speaker = random.sample(self.speakers, 1)[0]
      if self.num_utter_per_speaker > len(
          self.speaker_to_utters[speaker]
      ):
          utters = random.choices(
              self.speaker_to_utters[speaker],
              k=self.num_utter_per_speaker,
          )
      else:
          utters = random.sample(
              self.speaker_to_utters[speaker],
              self.num_utter_per_speaker,
          )
      return speaker, utters
  
file_path: TTS/TTS/speaker_encoder/dataset.py
imports: import numpy
  import numpy
  import queue
  import torch
  import random
  from torch.utils.data import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling the MyDataset obj's underlying member method code with example usage: obj.__sample_speaker() and returns: speaker,  utters